Assessing flash flood erosion following storm Daniel in Libya

The eastern Mediterranean basin is witnessing increased storm activity impacting populous urban coastal areas that historically were not prone to catastrophic flooding. In the fall of 2023, Storm Daniel struck the eastern coast of Libya, causing unprecedented flash floods with a tragic death toll and large-scale infrastructure damages. We use Sentinel-1A C-band SAR images to characterize the resulting flash flood erosion and sediment load dynamics across the watersheds and to map damages within coastal cities at their outlets. Our results suggest that sediment loading, resulting from surface erosion, increased the density of turbid streams. The above exacerbated the catastrophic impact of the flash floods in the coastal cities of Derna and Susah, where 66% and 48% of their respective urban surface have experienced moderate-to-high damages. Our findings highlight the increased vulnerability of coastal watersheds in arid areas within the eastern Mediterranean basin due to the forecasted increase in hydroclimatic extremes and call for a transformative coastal management approach to urgently implement nature-based solutions and land-use changes to mitigate these rising risks.


Comparison of the flood erosion/deposits with the land cover
First, we simplify the Food and Agriculture Organization (FAO) classes of the land cover map 75 for the ROI to facilitate comparison with our flood erosion/deposits map derived from CCD-PCA.The simplified classes are detailed in Supplementary Table 1.Notably, shrublands (37.7 %), grasslands-croplands (10.2 %), and bare areas (50.3 %) account for 98.2 % of the ROI's surface area, while urban areas contribute only to 1.4%.Moreover, Supplementary Table reveals that flood erosion primarily affects shrublands (54.3 %), bare areas (32 %), and croplands (12.3 %); however, we cannot interpret these numbers because of the high spatial correlation between the land cover distribution and the storm trajectory within the ROI, from North-West to South-East, as depicted in Supplementary Fig. 1, where shrublands received most of the precipitation.To mitigate this correlation, we normalize these values for each class to obtain the magnitude of surface change within each land cover class.Thus, 25.8 % of the shrublands, 21.6 % of the croplands, 11.4 % of the bare areas, and 10.8 % of urban areas are affected by flood erosion/deposits/damages.The last figure is significant compared to the 1.4 % urban coverage; this illustrates the catastrophic impact of the landfall on infrastructures and human lives.
FAO class definition 75  Supplementary Fig. 1.Simplified land cover 75 and flood erosion/deposits/damages derived from CCD-PCA.In particular, 54.3 % of the eroded areas (in red) are located within the Shrublands (in brown) and 25.8 % of shrublands were eroded by the flood (see Supplementary Table 1).Moreover, 32 % of the eroded areas are located within the bare areas (in yellow) and 11.4 % of bare areas were eroded.

Validation of CCD-PCA in Derna and Susah
We conduct this quantitative validation analysis to compare our damage map derived from CCD-PCA with the damage assessment maps provided by CEMS, including building, bridge and road damage assessments.To start, we establish a spatial unit of the same size for the maps, opting to downscale the datasets to a 50 m × 50 m spatial unit.Then, we intersect (refer to (4)) the occurrences of radar changes with the occurrences of any damage within the CEMS maps for each downscaled cell.It is worth noting that we conduct this spatial intersection twice: first, by considering destroyed buildings, and second, by considering both damaged and destroyed buildings.
From this spatial comparison, we extract and list in Supplementary Table 5 the "truedetection", "omission", and false detection." Where CCD is the damage map derived from CCD-PCA, "building", "bridge", and "road" refer to the CEMS maps of damaged/destroyed buildings, destroyed bridges, and destroyed roads, respectively.
The CEMS dataset encompasses the two following damage scenari:  Case 1: With damaged or destroyed buildings, destroyed bridges, damaged and destroyed roads  Case 2: With destroyed buildings, destroyed bridges, damaged and destroyed roads The validation of our CCD-PCA with the CEMS dataset in Derna (see Supplementary Fig. 2) shows that the CCD-PCA is valid for 60.9 % of the area for Case 1 (see Supplementary Fig. 2c) and 65.5 % for Case 2 (see Supplementary Fig. 2d).Moreover, in Susah, we show that the CCD-PCA is valid for 42 % of the area for Case 1 (see Supplementary Fig. 3b) and 64.5 % for Case 2 (see Supplementary Fig. 3d).This validation confirms that "destroyed buildings" are more detectable from the radar CCD-PCA method than "damaged buildings".We attribute this sensitivity to the difference in phase variability for each class of damage magnitude.Specifically, "destroyed buildings" are associated with significant phase spatial variability, while "damaged buildings" exhibit a more subtle phase spatial variability.

Spatial resolution
After a multilooking of 2 in azimuth direction and 7 in range direction, the Sentinel-1 SLC pixel spacing of 3.9 m × 13.9 m in range and azimuth, respectively, becomes a spatial resolution of 27 m.Then, using the adaptive nonlocal-InSAR (ANL-InSAR) spatial filtering 76 with a kernel varying from 3 to 7, depending on the InSAR variability, we obtain a final spatial resolution ranging from 81 m to 189 m.

Georeferencing and spatial accuracy
Overlapping products derived from radar imagery with optical images presents challenges due to geometric distortions from the layover and shadow effects arising from topography and look angle 77,78 .While georeferencing with a Digital Elevation Model (DEM) resolves the issue of overlapping, it also introduces distortions by stretching and shrinking radar images in areas of layovers and shadows, which are common in areas characterized by steep terrain, such as canyons, gullies and mountainous regions.This limitation could impact the identification of flood erosion, as it may alter the dimensions of areas experiencing surface changes.
To mitigate the above-mentioned distortion, our first approach is to georeference the radar CCD-PCA products without relying on a DEM but by selecting 118 ground control points (GCPs) distributed over the ROI.This georeferencing results in a root mean square (RMS) error of 177 m (spatial accuracy).While the georeferencing is suitable for some regions of the ROI, it falls short in other parts where the radar product shows significant spatial inconsistencies.Indeed, by adding this spatial accuracy to the spatial resolution, we obtain an error spanning from 258 m to 366 m.
As a result, we choose to georeference the radar products with the DEM, despite the potential distortion effects limiting the interpretation of flood erosion.

Requirement for effective PCA in the CCD
In our investigation, we base our CCD approach on Principal Component Analysis (PCA), which is primarily designed to capture linear relationships within a dataset.
To assess the linearity of the temporal coherence decorrelation, we produce a coherence time-series based on 69 SAR images (i.e., Sentinel-1A Descending, relative orbit number #7, with a 12-day acquisition interval) for the 2021-07-01 to 2023-09-25 period.We observe that the temporal coherence decorrelation follows a linear decay trend within a period of up to 72-84 days (see Supplementary Fig. 8; the coherence time-series is extracted from our sub-region of interest presented in Supplementary Fig. 9).Beyond this period, the trend transitions to an exponential decay associated with an annual periodic component.
Therefore, applying PCA on coherence pairs with a temporal baseline up to 72 days is acceptable for our sub-region of interest; the first principal component summarizes this linearity in temporal decorrelation, while the second principal component summarizes the remaining residuals (i.e., the CCD itself).Supplementary Fig. 10.Erosion hotspots assessment.Erosion hotspots assessment (in pink) performed within a 2 km × 2 km cell, encompassing hazard ( flood surface change derived from CCD in red and rainfall accumulation in dash blue) and vulnerability variables (topographic slopes in yellow).

Identification of wadis
To delineate the wadi networks within the ROI, we employ the following methodology using ArcGIS software.First, we fill any possible topographic depressions in the NASA DEM, a crucial step ensuring continuous run-off flow.Then, using the D8 method, we determine the flow direction from each cell by identifying the steepest gradient among the eight adjacent cells 79 .Next, we compute the flow accumulation for each cell.Finally, we determine the hierarchical order of wadis in the network using the Strahler stream order method 80 .
2. Validation of CCD-PCA in Derna for two scenari within the flooding extent: Case 1 and Case 2. a. Damaged and destroyed buildings, destroyed bridges, damaged and destroyed roads (Case 1) assessed by CEMS and overlapped over the CCD-PCA observations.b.Damaged buildings only, destroyed bridges, damaged and destroyed roads (Case 2), assessed by CEMS and overlapped over the CCD-PCA observations.c.Validation of CCD-PCA for Case 1. d. Validation of CCD-PCA for Case 2. The true detection-occurrence corresponds to the validation of the CCD-PCA with the infrastructure damages and/or destructions assessed by CEMS within a 50 m × 50 m cell, while true detection-no damage, signifies the absence of damage for both CEMS and CCD-PCA observations.The CCD-PCA is valid for 60.9 % of the area in Case 1 (frame c), and 65.5 % in Case 2 (frame d).

Table 3 ,
presented below, enumerate the SAR and Multispectral images utilized in this study to identify and visualize flood erosion.Moreover, Supplementary Table4lists the selection of InSAR pairs used in the CCD method.

Table 3 . C-band SAR and multispectral images used for visualization only.
Supplementary Table 4.